PAMA
1.2.0Rank Aggregation with Partition Mallows Model
Overview
Rank aggregation aims to achieve a better ranking list given multiple observations. 'PAMA' implements Partition-Mallows model for rank aggregation where the rankers' quality are different. Both Bayesian inference and Maximum likelihood estimation (MLE) are provided. It can handle partial list as well. When covariates information is available, this package can make inference by incorporating the covariate information. More information can be found in the paper "Integrated Partition-Mallows Model and Its Inference for Rank Aggregation".
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People & History
6 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.3.0 released · 2023-04-21
- archivedRemoved from CRAN2022-11-20requires archived package 'PerMallows'
- RR 4.2.0 released · 2022-04-22
- RR 4.1.0 released · 2021-05-18
- 1.2.02021-05-06 · diff ↗
- 1.1.02021-04-28 · diff ↗
- 1.0.12021-04-18 · diff ↗
- 1.0.02021-04-14 · diff ↗
- RR 4.0.0 released · 2020-04-24
- 0.1.12020-04-09 · diff ↗
- 0.1.02020-01-13
- RR 3.6.0 released · 2019-04-26
Package metadata
- Total releases
- 6
- License
- GPL (>= 2) OSI
- Minimum R
- ≥ 3.1.0
- Download size
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